Development of a nomogram for predicting recurrence of epithelial ovarian cancer involving traditional Chinese medicine treatment.

Background: The treatment of epithelial ovarian cancer (EOC) is evolving towards personalization and precision. Early prediction of recurrence can provide a basis for individualized monitoring and treatment. Our study aims to develop a predictive model for early recurrence of ovarian cancer incorporating Traditional Chinese Medicine (TCM) treatment.

Methods: We reviewed the clinicopathological and prognostic data of EOC patients who achieved complete clinical remission after surgery and chemotherapy at Guangdong Traditional Chinese Medicine Hospital (GPHCM) between December 2011 and July 2022. Basic information, clinical characteristics, treatment plans, and follow-up data were collected. Univariate logistic analysis was performed to identify significant variables (<0.10), followed by Least Absolute Shrinkage and Selection Operator (LASSO) regression to further determine key risk factors. A multivariate logistic regression model was constructed based on these factors, and a nomogram was developed to predict recurrence risk. The model's effectiveness was internally validated using bootstrap resampling (1000 iterations) and assessed for discrimination and calibration using Area Under Curve (AUC), the Hosmer-Lemeshow test, and calibration plots. Additionally, decision curve analysis (DCA) was performed to evaluate the clinical utility of the model.

Result: This study included a total of 170 patients. Multivariate logistic regression analysis revealed that surgical procedure, The International Federation of Gynecology and Obstetrics (FIGO) stage, completion of the full chemotherapy course, and exposure to TCM were independent prognostic factors for ovarian cancer recurrence. Based on these factors, this study developed a nomogram model to predict recurrence risk, incorporating four key variables. The AUC of the prediction model was 0.843 (95% CI: 0.774-0.898), and the Hosmer-Lemeshow test and calibration plot indicated good calibration. DCA showed the model provided higher net benefit across a wide range of threshold probabilities.

Conclusion: The nomogram we developed effectively predicted 2-year recurrence risk in epithelial ovarian cancer patients. Notably, TCM treatment lasting more than 6 months may help prolong progression-free survival (PFS).